In reaction to this need, the central goal of METAL is to develop a prototype assistant system that supports users with model selection and method combination, and guides them through the space of experiments. The system will combine prior meta-knowledge with meta-level learning. For each constituent, known techniques will be consolidated and original ones developed to cope with novel learning situations and applications. The assistant's meta-knowledge base, which integrates expert-given meta-information and the results of meta-learning, provides key information about past usage of ML systems. This knowledge describes the conditions under which operations carried out in the past have succeeded or failed. The proposed system is to exploit this information when providing guidance to the user. Such guidance will not be restricted to selection of an appropriate method only, but will also suggest data transformation steps that are often crucial to obtain good results. Furthermore, the system's guidance will not be limited to a single choice. It may consist of a set of promising operations that the user may readily explore in an orderly fashion. The system will extend its meta-knowledge base dynamically, as it is used, and hence have the capability to adapt to specific environments.
The expected effects, and the criteria by which the success of the project will be measured, are improved utility of data mining tools and in particular a significant savings in experimentation time. METAL is not only at the forefront of science, it also provides a promising solution to many practical problems. Results of the project will be reported in a book and the prototype will be accompanied by a technical manual for use by developers from the European software industry who would decide to implement it as a commercial tool.
The project's home page is here.